如何使用Python对网易云歌单数据分析及可视化

from bs4 import BeautifulSoup  import requests  import time    headers = {      'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'  }    for i in range(0, 1330, 35):      print(i)      time.sleep(2)      url = 'https://music.163.com/discover/playlist/?cat=华语&order=hot&limit=35&offset=' + str(i)#修改这里即可      response = requests.get(url=url, headers=headers)      html = response.text      soup = BeautifulSoup(html, 'html.parser')      # 获取包含歌单详情页网址的标签      ids = soup.select('.dec a')      # 获取包含歌单索引页信息的标签      lis = soup.select('#m-pl-container li')      print(len(lis))      for j in range(len(lis)):          # 获取歌单详情页地址          url = ids[j]['href']          # 获取歌单标题          title = ids[j]['title']          # 获取歌单播放量          play = lis[j].select('.nb')[0].get_text()          # 获取歌单贡献者名字          user = lis[j].select('p')[1].select('a')[0].get_text()          # 输出歌单索引页信息          print(url, title, play, user)          # 将信息写入CSV文件中          with open('playlist.csv', 'a+', encoding='utf-8-sig') as f:              f.write(url + ',' + title + ',' + play + ',' + user + '
')    from bs4 import BeautifulSoup  import pandas as pd  import requests  import time    df = pd.read_csv('playlist.csv', header=None, error_bad_lines=False, names=['url', 'title', 'play', 'user'])    headers = {      'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'  }    for i in df['url']:      time.sleep(2)      url = 'https://music.163.com' + i      response = requests.get(url=url, headers=headers)      html = response.text      soup = BeautifulSoup(html, 'html.parser')      # 获取歌单标题      title = soup.select('h3')[0].get_text().replace(',', '，')      # 获取标签      tags = []      tags_message = soup.select('.u-tag i')      for p in tags_message:          tags.append(p.get_text())      # 对标签进行格式化      if len(tags) > 1:          tag = '-'.join(tags)      else:          tag = tags[0]      # 获取歌单介绍      if soup.select('#album-desc-more'):          text = soup.select('#album-desc-more')[0].get_text().replace('
', '').replace(',', '，')      else:          text = '无'      # 获取歌单收藏量      collection = soup.select('#content-operation i')[1].get_text().replace('(', '').replace(')', '')      # 歌单播放量      play = soup.select('.s-fc6')[0].get_text()      # 歌单内歌曲数      songs = soup.select('#playlist-track-count')[0].get_text()      # 歌单评论数      comments = soup.select('#cnt_comment_count')[0].get_text()      # 输出歌单详情页信息      print(title, tag, text, collection, play, songs, comments)      # 将详情页信息写入CSV文件中      with open('music_message.csv', 'a+', encoding='utf-8') as f:          # f.write(title + '/' + tag + '/' + text + '/' + collection + '/' + play + '/' + songs + '/' + comments + '
')          f.write(title + ',' + tag + ',' + text + ',' + collection + ',' + play + ',' + songs + ',' + comments + '
')

df['collection'] = df['collection'].astype('string').str.strip()  df['collection'] = [int(str(i).replace('万','0000')) for i in df['collection']]  df['text'] = [str(i)[3:] for i in df['text']]  df['comments'] = [0 if '评论' in str(i).strip() else int(i) for i in df['comments']]

df_play = df[['title','play']].sort_values('play',ascending=False)  df_play[:10]  df_play = df_play[:10]  _x = df_play['title'].tolist()  _y = df_play['play'].tolist()  df_play = get_matplot(x=_x,y=_y,chart='barh',title='网易云音乐华语歌单播放 TOP10',ha='left',size=8,color=color[0])  df_play

df_col = df[['title','collection']].sort_values('collection',ascending=False)  df_col[:10]  df_col = df_col[:10]  _x = df_col['title'].tolist()  _y = df_col['collection'].tolist()  df_col = get_matplot(x=_x,y=_y,chart='barh',title='网易云音乐华语歌单收藏 TOP10',ha='left',size=8,color=color[1])  df_col

df_com = df[['title','comments']].sort_values('comments',ascending=False)  df_com[:10]  df_com = df_com[:10]  _x = df_com['title'].tolist()  _y = df_com['comments'].tolist()  df_com = get_matplot(x=_x,y=_y,chart='barh',title='网易云音乐华语歌单评论数 TOP10',ha='left',size=8,color=color[2])  df_com

df_songs = np.log(df['songs'])  df_songs  df_songs = get_matplot(x=0,y=df_songs,chart='hist',title='华语歌单歌曲收录分布情况',ha='left',size=10,color=color[3])  df_songs

def get_tag(df):      df = df['tag'].str.split('-')      datalist = list(set(x for data in df for x in data))      return  datalist  df_tag = get_tag(df)  # df_tag  def get_lx(x,i):      if i in str(x):          return 1      else:          return 0  for i in list(df_tag):#这里的df['all_category'].unique()也可以自己用列表构建，我这里是利用了前面获得的      df[i] = df['tag'].apply(get_lx,i=f'{i}')  # df.head()  Series = df.iloc[:,7:].sum().sort_values(0,ascending=False)  df_tag = [tag for tag in zip(Series.index.tolist(),Series.values.tolist())]  df_tag[:10]  df_iex = [index for index in Series.index.tolist()][:20]  df_tag = [tag for tag in Series.values.tolist()][:20]  df_tagiex = get_matplot(x=df_iex,y=df_tag,chart='plot',title='网易云音乐华语歌单标签图',size=10,ha='center',color=color[3])  df_tagiex

df_user = pd.read_csv('playlist.csv',encoding="unicode_escape",header=0,names=['url','title','play','user'],sep=',')  df_user.shape  df_user = df_user.iloc[:,1:]  df_user['count'] = 0  df_user = df_user.groupby('user',as_index=False)['count'].count()  df_user = df_user.sort_values('count',ascending=False)[:10]  df_user  df_user = df_user[:10]  names = df_user['user'].tolist()  nums = df_user['count'].tolist()  df_u = get_matplot(x=names,y=nums,chart='barh',title='歌单贡献UP主 TOP10',ha='left',size=10,color=color[4])  df_u

import wordcloud  import pandas as pd  import numpy as np  from PIL import Image  data = pd.read_excel('music_message.xlsx')  #根据播放量排序，只取前五十个  data = data.sort_values('play',ascending=False).head(50)    #font_path指明用什么样的字体风格，这里用的是电脑上都有的微软雅黑  w1 = wordcloud.WordCloud(width=1000,height=700,                           background_color='black',                           font_path='msyh.ttc')  txt = "
".join(i for i in data['title'])  w1.generate(txt)  w1.to_file('F:\词云.png')

def get_matplot(x,y,chart,title,ha,size,color):      # 设置图片显示属性,字体及大小      plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']      plt.rcParams['font.size'] = size      plt.rcParams['axes.unicode_minus'] = False      # 设置图片显示属性      fig = plt.figure(figsize=(16, 8), dpi=80)      ax = plt.subplot(1, 1, 1)      ax.patch.set_color('white')      # 设置坐标轴属性      lines = plt.gca()      # 设置显示数据      if x ==0:          pass      else:          x.reverse()          y.reverse()          data = pd.Series(y, index=x)      # 设置坐标轴颜色      lines.spines['right'].set_color('none')      lines.spines['top'].set_color('none')      lines.spines['left'].set_color((64/255, 64/255, 64/255))      lines.spines['bottom'].set_color((64/255, 64/255, 64/255))      # 设置坐标轴刻度      lines.xaxis.set_ticks_position('none')      lines.yaxis.set_ticks_position('none')      if chart == 'barh':          # 绘制柱状图,设置柱状图颜色          data.plot.barh(ax=ax, width=0.7, alpha=0.7, color=color)          # 添加标题,设置字体大小          ax.set_title(f'{title}', fontsize=18, fontweight='light')          # 添加歌曲出现次数文本          for x, y in enumerate(data.values):              plt.text(y+0.3, x-0.12, '%s' % y, ha=f'{ha}')      elif chart == 'hist':          # 绘制直方图,设置柱状图颜色          ax.hist(y, bins=30, alpha=0.7, color=(21/255, 47/255, 71/255))          # 添加标题,设置字体大小          ax.set_title(f'{title}', fontsize=18, fontweight='light')      elif chart == 'plot':          colors = ['#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff', '#f7bb5f', '#eafb50',           '#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff', '#f7bb5f', '#eafb50',           '#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff', '#f7bb5f', '#eafb50',           '#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff', '#f7bb5f', '#eafb50',           '#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff', '#f7bb5f', '#eafb50',            '#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff', '#f7bb5f', '#eafb50',            '#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff']          plot = squarify.plot(sizes=y, label=x, color=colors, alpha=1, value=y, edgecolor='white', linewidth=1.5)          # 设置标签大小为1          plt.rc('font', size=6)          # 设置标题大小          plot.set_title(f'{title}', fontsize=13, fontweight='light')          # 除坐标轴          plt.axis('off')          # 除上边框和右边框刻度          plt.tick_params(top=False, right=False)      # 显示图片      plt.show()  #构建color序列  color = [(153/255, 0/255, 102/255),(8/255, 88/255, 121/255),(160/255, 102/255, 50/255),(136/255, 43/255, 48/255),(16/255, 152/255, 168/255),(153/255, 0/255, 102/255)]